Catalog data quality
Catalog quality starts with an explainable product record
A practical rhythm for checking source data, shaping one approved product record—canonical product truth—and turning quality issues into reviewable work.
A practical guide for catalog, ecommerce, and product-data practitioners.
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Validate the source before interpreting it
Good catalog work begins before AI content drafting or scoring. A team needs to know which source supplied each row, how incoming columns map to product fields, and which values failed validation. Without that context, a polished record can still conceal a broken import.
Validation should identify the exact row, field, and rule involved. Mapping decisions should be reviewable before commit so a source column cannot silently become the wrong canonical attribute across an entire catalog.
Practice
- Preserve source identity and row-level lineage.
- Preview mapping decisions before committing them.
- Turn validation failures into specific corrective work.
“Catalog quality becomes actionable when every issue points back to a source, an owner, and the next decision.”
Commit a canonical structure deliberately
Products, variants, assets, attributes, and external identifiers each answer a different question. Keeping those records explicit gives the team one product identity without flattening size, color, imagery, and source provenance into a single fragile row.
A commit should create canonical product truth only after validation has passed. Re-imports can then reconcile against stable identifiers instead of duplicating products or erasing where a value came from.
Practice
- Keep product and variant identity separate.
- Retain external identifiers for later reconciliation.
- Commit only a reviewed and validated mapping.
Turn quality issues into reviewable work
Quality checks are most useful when they identify the product, the issue, its severity, and the action needed to resolve it. Missing copy, unusable assets, conflicting variants, and stale source evidence should not collapse into one unexplained score.
The same issue may carry different importance for different destinations. Separating the underlying finding from a channel profile lets the team fix product truth once and understand where that fix removes a blocker.
Practice
- Name the affected product field or asset.
- Keep severity separate from channel priority.
- Resolve issues against canonical truth, not a display-only patch.
Recheck quality at the channel edge
A clean canonical record still needs to be evaluated against the destination that will consume it. Required fields, localization, asset rules, and approved content can vary by channel, so readiness should explain the remaining gaps rather than declare a universal quality score.
Finish with a destination view. A preview can expose hierarchy, language, or image-order problems that a field checklist misses, while the export state remains separate from proof of publication.
Practice
- Evaluate approved truth against named channel requirements.
- Resolve blockers before polishing optional fields.
- Inspect the destination view before preparing export.
Operating principles
What the method protects.
Source lineage stays attached
Tessaire preserves source identity and external references alongside canonical products and variants.
Review preserves canonical truth
Source values, suggestions, review decisions, QA issues, and approved product attributes remain distinct records.
Channel simulation is not publication
The simulator helps teams inspect channel presentation without writing to a marketplace or storefront.
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